GPS-free autonomous navigation in cluttered tree rows with deep semantic segmentation
Year: 2024
Type: article
Abstract: Segmentation-based autonomous navigation has recently been presented as an appealing approach to guiding robotic platforms through crop rows without requiring perfect GPS localization. Nevertheless, current techniques are restricted to situations where the distinct separation between the plants and the sky allows for the identification of the row’s center. However, tall, dense vegetation, such as high tree rows and orchards, is the primary cause of GPS signal blockage. In this study, we increase the overall robustness and adaptability of the control algorithm by extending the segmentation-based robotic guiding to those cases where canopies and branches occlude the sky and prevent the utilization of GPS and earlier approaches. An efficient Deep Neural Network architecture has been used to address semantic segmentation, performing the training with synthetic data only. Numerous vineyards and tree fields have undergone extensive testing in both simulation and real world to show the solution’s competitive benefits. The system achieved unseen results in orchards, with a Mean Average Error smaller than 9% of the maximum width of each row, improving state-of-the-art algorithms by disclosing new scenarios such as close canopy crops. The official code can be found at: https://github.com/PIC4SeR/SegMinNavigation.git . • We propose two segmentation-based methods for autonomous navigation in orchards. • Our algorithms are tested on unseen crops, such as vineyards and fruit orchards. • We trained a neural network with a synthetic dataset, ensuring generalization. • The new methods outperform state-of-the-art solutions in simulated and real vineyards.
Source: Robotics and Autonomous Systems
Institution: Politecnico di Torino
Language: English
Topic: Smart Agriculture and AI
Subfield: Plant Science
Domain: Life Sciences
Open Access status: hybrid
Estimated APC paid: $3,050
Awards: G6327910654, G7055482998
OpenAlex ID: W4404171810